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Machine learning techniques to characterize functional traits of plankton from image data

Eric C Orenstein 1 Sakina-Dorothée Ayata 1, * Frédéric Maps 2 Érica C Becker 3 Fabio Benedetti 4 Tristan Biard 5 Thibault de Garidel-Thoron 6 Jeffrey S Ellen 7 Filippo Ferrario 2 Sarah L C Giering 8 Tamar Guy-Haim 9 Laura Hoebeke 10 Morten Hvitfeldt Iversen 11 Thomas Kiørboe 12 Jean-François Lalonde 13 Arancha Lana 14 Martin Laviale 15 Fabien Lombard 1, 16 Tom Lorimer 17 Severine Martini 18 Albin Meyer 15 Klas Ove Möller 19 Barbara Niehoff 11 Mark D Ohman 7 Cedric Pradalier 20 Jean-Baptiste Romagnan 21 Simon-Martin Schröder 22 Virginie Sonnet 23 Heidi M Sosik 24 Lars S Stemmann 1 Michiel Stock 10 Tuba Terbiyik-Kurt 25 Nerea Valcárcel-Pérez 26 Laure Vilgrain 1 Guillaume Wacquet 27 Anya M Waite 28 Jean-Olivier Irisson 1 
* Corresponding author
Abstract : Plankton imaging systems supported by automated classification and analysis have improved ecologists' ability to observe aquatic ecosystems. Today, we are on the cusp of reliably tracking plankton populations with a suite of lab-based and in situ tools, collecting imaging data at unprecedentedly fine spatial and temporal scales. But these data streams have potential well beyond examining the abundances of different taxa; the individual images themselves contain a wealth of information on functional traits. Here we outline traits that could be measured from image data, suggest computer vision approaches to extract functional trait information from the images, and discuss promising avenues for novel studies. The approaches we discuss are data agnostic and are broadly applicable to other aquatic or terrestrial organisms.
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Contributor : Martin Laviale Connect in order to contact the contributor
Submitted on : Thursday, June 30, 2022 - 8:18:45 PM
Last modification on : Friday, August 5, 2022 - 12:36:12 PM


Limnology Oceanography - 2022 ...
Publication funded by an institution


Distributed under a Creative Commons Attribution 4.0 International License



Eric C Orenstein, Sakina-Dorothée Ayata, Frédéric Maps, Érica C Becker, Fabio Benedetti, et al.. Machine learning techniques to characterize functional traits of plankton from image data. Limnology and Oceanography, Association for the Sciences of Limnology and Oceanography, In press, 9999, pp.1-23. ⟨10.1002/lno.12101⟩. ⟨hal-03482282v2⟩



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